Cardiac arrhythmia detection in ECG signals by feature Extraction and support vector machine

Purpose of this work is to develop an automated physiological signal diagnostic tool that can help us to early determination of arrhythmia for proper medical attention. This paper presents a simple automated approach for classification of normal and abnormal ECG based on arrhythmia. The proposed method validated by the data MIT BIH arrhythmia database. The performance in terms of accuracy for clinical decision must be very high. This method uses fourth order wavelet decomposition, wavelet decomposition used for time frequency representation and feature extraction. For classification support vector machine is used for detection kinds of ECG signals

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Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024)